A knowledge graph memory server implemented in Swift, providing persistent memory functions for large - language models

Rememberizer's MCP server provides the ability to interact with document and knowledge management APIs for large language models

A memory server implementing the Model Context Protocol (MCP), providing persistent memory storage and retrieval functions based on a knowledge graph for large language models.

This project is a document processing server based on the Model Context Protocol (MCP) standard. By building a vector database and an MCP interface, it enables AI assistants to access external document resources and break through the knowledge limitations of large language models. The project includes two major components: a document processing pipeline and an MCP server. It supports multiple embedding models and file formats and can be applied to scenarios such as querying the latest technical documents and understanding private code libraries.

This project implements an MCP server for interacting with the Wolfram Alpha API, enabling chat applications to perform calculation queries and obtain structured knowledge. It includes an MCP client example based on Gemini, demonstrating how to connect a large - language model to the MCP server for real - time interaction with the Wolfram Alpha knowledge engine.

This project builds a Retrieval Augmented Generation (RAG) server based on IBM Watsonx.ai, uses ChromaDB for vector indexing, and exposes interfaces through the Model Context Protocol (MCP). The system can process PDF documents and answer questions based on the document content, realizing an intelligent question answering function that combines large language models with specific domain knowledge.

KIP is a graph interaction protocol designed for large language models, connecting LLMs with knowledge graphs and providing capabilities for memory persistence, knowledge evolution, and interpretable interaction.

The Model Context Protocol (MCP) is an open - source protocol released by Anthropic, aiming to solve the standardization problem of connecting large language models (LLMs) with external data sources and systems. It provides a structured framework that enables models to integrate and utilize external context, thereby expanding their capabilities and improving response accuracy. MCP supports functions such as knowledge expansion, external tool calls, and pre - written prompts.

The Model Context Protocol (MCP) is a standardized protocol introduced by Anthropic for the interaction between large language models (LLMs) and external tools and functions, as well as for injecting context into LLMs. It enables models to request real-time information, perform operations in external systems, access professional knowledge bases, and interact with APIs and services. The MCP is similar to a USB hub, serving as a standard interface that reduces the risk of API changes because each MCP server is maintained by its own company, and users only need to integrate it. The MCP server provides three functions: tools, resources, and prompts.

The HUDU MCP Server is a model context protocol server integrated with the HUDU technical documentation and customer information management system, allowing large language models to obtain customer account information, knowledge base articles, asset data, etc. through API interaction.

A server based on the Model Context Protocol for interacting with Rememberizer's document and knowledge management API, enabling large - language models to search, retrieve, manage documents, and integrate.

A knowledge graph builder that uses a local AI model to convert text or web page content into structured knowledge graphs, supporting large-scale content processing, real-time visualization, and integration with Neo4j and Qdrant databases.

Librarian is a knowledge base server based on the MCP protocol, providing reading, search, and retrieval functions for structured documents for large language models. It organizes document content through a tag system and directory hierarchy.

Awesome MCP Servers is a carefully curated collection of MCP (Model Context Protocol) servers to extend the capabilities of large language models (LLMs). This project lists various MCP server implementations by function, covering multiple professional domains such as databases, fintech, and knowledge memory, and provides development framework resources.

An intelligent tool scheduling system based on FastMCP 2.0. The large model can autonomously call over 26 tools, supporting knowledge graphs, vector search, and real - time progress display, and enabling automation of enterprise knowledge management and business data analysis.

This is an MCP server for interacting with the Agile Practice Map. It retrieves information about agile practices from the knowledge base through a large - language model and provides query, listing, and resource access functions.

Basic Memory is a tool for building persistent knowledge through natural conversations with large language models (LLMs). It stores data locally in Markdown files and implements the MCP protocol for two - way knowledge management.